{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"youtube_8m_frame_level_datasets.ipynb","version":"0.3.2","provenance":[]},"language_info":{"codemirror_mode":{"name":"ipython","version":2},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython2","version":"2.7.15"},"kernelspec":{"display_name":"Python 2","language":"python","name":"python2"}},"cells":[{"metadata":{"deletable":true,"editable":true,"id":"yg7wMoyKnHFF","colab_type":"code","colab":{},"outputId":"e0755c43-c24d-45e8-99aa-96366c4d4868"},"cell_type":"code","source":["# Import libraries and modules\n","import tensorflow as tf\n","import numpy as np\n","import pandas as pd\n","import shutil\n","print tf.__version__\n","print np.__version__\n","print pd.__version__\n","np.set_printoptions(threshold=np.inf)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["1.8.0\n","1.14.0\n","0.22.0\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/envs/py2env/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n","  from ._conv import register_converters as _register_converters\n"],"name":"stderr"}]},{"metadata":{"deletable":true,"editable":true,"id":"qrlaQMynnHFM","colab_type":"code","colab":{}},"cell_type":"code","source":["# change these to try this notebook out\n","BUCKET = 'youtube8m-4-train'\n","PROJECT = 'qwiklabs-gcp-8d3d0cd07cef9252'\n","REGION = 'us-central1'"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"h-3hOowMnHFO","colab_type":"code","colab":{}},"cell_type":"code","source":["# Import os environment variables\n","import os\n","os.environ['BUCKET'] = BUCKET\n","os.environ['PROJECT'] = PROJECT\n","os.environ['REGION'] = REGION"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"5krJtT6knHFR","colab_type":"text"},"cell_type":"markdown","source":["# Local Development"]},{"metadata":{"deletable":true,"editable":true,"id":"S69fx6HTnHFS","colab_type":"code","colab":{}},"cell_type":"code","source":["# Set logging verbosity to INFO for richer output\n","tf.logging.set_verbosity(tf.logging.INFO)"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"R0nLDG2LnHFU","colab_type":"code","colab":{}},"cell_type":"code","source":["# The number of video classes\n","NUM_CLASSES = 4716\n","# The number of frames to extract per video\n","MAX_FRAMES = 20"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"WERxFDbPnHFW","colab_type":"code","colab":{}},"cell_type":"code","source":["arguments = {}\n","arguments[\"train_file_pattern\"] = \"gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\"\n","arguments[\"eval_file_pattern\"] = \"gs://youtube-8m-team/1/frame_level/validate/validate-0.tfrecord\"\n","arguments[\"output_dir\"] = \"trained_model\"\n","arguments[\"batch_size\"] = 10\n","arguments[\"train_steps\"] = 100\n","arguments[\"hidden_units\"] = [1024, 256, 64]\n","arguments[\"top_k\"] = 5\n","arguments[\"start_delay_secs\"] = 60\n","arguments[\"throttle_secs\"] = 30"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"enmF7EyNnHFZ","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create input function to read our training and validation data\n","def read_dataset_frame(file_pattern, mode, batch_size):\n","  def _input_fn():\n","    print(\"\\nread_dataset_frame: _input_fn: file_pattern = {}\".format(file_pattern))\n","    print(\"read_dataset_frame: _input_fn: mode = {}\".format(mode))\n","    print(\"read_dataset_frame: _input_fn: batch_size = {}\".format(batch_size))\n","    \n","    # This function dequantizes our tensors to bring them back to full floating point precision\n","    def dequantize(feat_vector, max_quantized_value = 2, min_quantized_value = -2):\n","      assert max_quantized_value > min_quantized_value # ensure the max value is larger than the min value\n","      quantized_range = max_quantized_value - min_quantized_value # find the range between max and min\n","      scalar = quantized_range / 255.0 # create a scale factor where 0 is the min and 1 is the max\n","      bias = (quantized_range / 512.0) + min_quantized_value # create bias term to shift our scaled feature vector\n","      return feat_vector * scalar + bias # return the scaled and shifted feature vector\n","\n","    # This function resizes our frames axis so that we only get a subset of frames\n","    def resize_axis(tensor, axis, new_size, fill_value = 0):\n","      tensor = tf.convert_to_tensor(value = tensor) # ensure tensor is a tensor\n","      shape = tf.unstack(value = tf.shape(input = tensor)) # create a list where each element is a 1-D tensor the size of each dimension in tensor\n","\n","      pad_shape = shape[:] # create a copy of the shape list of 1-D tensors\n","      pad_shape[axis] = tf.maximum(x = 0, y = new_size - shape[axis]) # change the size of the axis dimension to the maximum of 0 and the new size of our padded shape\n","\n","      shape[axis] = tf.minimum(x = shape[axis], y = new_size) # change the size of the axis dimension to the minimum of our original shape and the new size of our padded shape\n","      shape = tf.stack(values = shape) # stack the list of tensor sizes back into a larger tensor\n","\n","      resized = tf.concat(values = [\n","        tf.slice(input_ = tensor, begin = tf.zeros_like(tensor = shape), size = shape), # slice the tensor starting at the 0th index in each dimension and going as far as our adjusted shape in each dimension\n","        tf.fill(dims = tf.stack(values = pad_shape), value = tf.cast(x = fill_value, dtype = tensor.dtype)) # fill the rest of the tensor with the fill value\n","      ], axis = axis) # concatenate our sliced tensor with our fill value tensor together\n","\n","      new_shape = tensor.get_shape().as_list() # get the static shape of the tensor and output it to a list\n","      new_shape[axis] = new_size # change the static shape's axis to our new size\n","      resized.set_shape(shape = new_shape) # set the static shape of our resized tensor to our new shape\n","      return resized # return the resized tensor\n","\n","    def decode_example(serialized_examples):\n","      # Create context and sequence feature map\n","      context_features = {\n","        \"video_id\": tf.FixedLenFeature(shape = [], dtype = tf.string),\n","        \"labels\": tf.VarLenFeature(dtype = tf.int64)\n","      }\n","      sequence_features = {\n","        \"rgb\": tf.FixedLenSequenceFeature(shape = [], dtype = tf.string),\n","        \"audio\": tf.FixedLenSequenceFeature(shape = [], dtype = tf.string)\n","      }\n","\n","      # Parse TF Records into our features\n","      contexts, features = tf.parse_single_sequence_example(\n","          serialized = serialized_examples, \n","          context_features = context_features,\n","          sequence_features = sequence_features)\n","      print(\"\\nread_dataset_frame: _input_fn: decode_example: contexts = {}\".format(contexts))\n","      print(\"read_dataset_frame: _input_fn: decode_example: features = {}\".format(features))\n","      \n","      # Create features\n","      # Pass video_id to features\n","      features['video_id'] = contexts['video_id']\n","      print(\"read_dataset_frame: _input_fn: decode_example: features = {}\".format(features))\n","      \n","      # Fix rgb data\n","      decoded_rgb = tf.reshape(tf.cast(tf.decode_raw(features[\"rgb\"], tf.uint8), tf.float32), [-1, 1024])\n","      print(\"read_dataset_frame: _input_fn: decode_example: decoded_rgb = {}\".format(decoded_rgb))\n","      rgb_matrix = resize_axis(tensor = dequantize(decoded_rgb), axis = 0, new_size = MAX_FRAMES)\n","      print(\"read_dataset_frame: _input_fn: decode_example: rgb_matrix = {}\".format(rgb_matrix))\n","      features['rgb'] = rgb_matrix\n","      print(\"read_dataset_frame: _input_fn: decode_example: features = {}\".format(features))\n","\n","      # Fix audio data\n","      decoded_audio = tf.reshape(tf.cast(tf.decode_raw(features[\"audio\"], tf.uint8), tf.float32), [-1, 128])\n","      print(\"read_dataset_frame: _input_fn: decode_example: decoded_audio = {}\".format(decoded_audio))\n","      audio_matrix = resize_axis(tensor = dequantize(decoded_audio), axis = 0, new_size = MAX_FRAMES)\n","      print(\"read_dataset_frame: _input_fn: decode_example: audio_matrix = {}\".format(audio_matrix))\n","      features['audio'] = audio_matrix\n","      print(\"read_dataset_frame: _input_fn: decode_example: features = {}\".format(features))\n","\n","      # Extract and format labels\n","      labels = tf.cast(x = tf.sparse_to_dense(sparse_indices = contexts['labels'].values, output_shape = (NUM_CLASSES,), sparse_values = 1, validate_indices = False), dtype = tf.float32)\n","      print(\"read_dataset_frame: _input_fn: decode_example: labels = {}\\n\".format(labels))\n","\n","      return features, labels\n","\n","    # Create list of files from file pattern\n","    file_list = tf.gfile.Glob(filename = file_pattern)\n","    #print(\"read_dataset_frame: _input_fn: file_list = {}\".format(file_list))\n","\n","    # Create dataset from file list\n","    dataset = tf.data.TFRecordDataset(filenames = file_list)\n","    print(\"read_dataset_frame: _input_fn: dataset.TFRecordDataset = {}\".format(dataset))\n","\n","    # Decode TF Record dataset examples\n","    dataset = dataset.map(map_func = lambda x: decode_example(serialized_examples = x))\n","    print(\"read_dataset_frame: _input_fn: dataset.map = {}\".format(dataset))\n","\n","    # Determine amount of times to repeat file and if we should shuffle based on if we are training or evaluating\n","    if mode == tf.estimator.ModeKeys.TRAIN:\n","      num_epochs = None # read files forever\n","      \n","      # Shuffle the dataset within a buffer\n","      dataset = dataset.shuffle(buffer_size = batch_size * 10, seed = None)\n","      print(\"read_dataset_frame: _input_fn: dataset.shuffle = {}\".format(dataset))\n","    else:\n","      num_epochs = 1 # read files only once\n","\n","    # Repeat files num_epoch times\n","    dataset = dataset.repeat(count = num_epochs)\n","    print(\"read_dataset_frame: _input_fn: dataset.repeat = {}\".format(dataset))\n","\n","    # Group the data into batches\n","    dataset = dataset.batch(batch_size = batch_size)\n","    print(\"read_dataset_frame: _input_fn: dataset.batch = {}\".format(dataset))\n","\n","    # Create a iterator and then pull the next batch of features and labels from the example queue\n","    batch_features, batch_labels = dataset.make_one_shot_iterator().get_next()\n","    print(\"read_dataset_frame: _input_fn: batch_features = {}\".format(batch_features))\n","    print(\"read_dataset_frame: _input_fn: batch_labels = {}\\n\".format(batch_labels))\n","\n","    return batch_features, batch_labels\n","  return _input_fn"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"MCo1PxONnHFb","colab_type":"code","colab":{}},"cell_type":"code","source":["def try_input_function():\n","  with tf.Session() as sess:\n","    fn = read_dataset_frame(file_pattern = \"gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\", mode = tf.estimator.ModeKeys.TRAIN, batch_size = 2)\n","    batch_features, batch_labels = fn()\n","    features, labels = sess.run([batch_features, batch_labels])\n","    \n","    print(\"\\ntry_input_function: features = {}\".format(features))\n","    print(\"try_input_function: labels = {}\\n\".format(labels))"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"g_KeH0JinHFe","colab_type":"code","colab":{},"outputId":"167fce8f-fe92-4cca-f1d8-cd2ec8d3af88"},"cell_type":"code","source":["try_input_function()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\n","read_dataset_frame: _input_fn: file_pattern = gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\n","read_dataset_frame: _input_fn: mode = train\n","read_dataset_frame: _input_fn: batch_size = 2\n","read_dataset_frame: _input_fn: dataset.TFRecordDataset = <TFRecordDataset shapes: (), types: tf.string>\n","\n","read_dataset_frame: _input_fn: decode_example: contexts = {'labels': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7f8e88710c10>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_rgb = Tensor(\"Reshape:0\", shape=(?, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: rgb_matrix = Tensor(\"concat:0\", shape=(20, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_audio = Tensor(\"Reshape_1:0\", shape=(?, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: audio_matrix = Tensor(\"concat_1:0\", shape=(20, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'concat_1:0' shape=(20, 128) dtype=float32>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: labels = Tensor(\"Cast_4:0\", shape=(4716,), dtype=float32)\n","\n","read_dataset_frame: _input_fn: dataset.map = <MapDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.shuffle = <ShuffleDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.repeat = <RepeatDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.batch = <BatchDataset shapes: ({rgb: (?, 20, 1024), audio: (?, 20, 128), video_id: (?,)}, (?, 4716)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: batch_features = {'rgb': <tf.Tensor 'IteratorGetNext_8:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext_8:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext_8:2' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: batch_labels = Tensor(\"IteratorGetNext_8:3\", shape=(?, 4716), dtype=float32)\n","\n","\n","try_input_function: features = {'rgb': array([[[ 1.7882049e+00, -9.4148159e-02,  3.6075377e-01,  2.8232241e-01,\n","         -1.5372856e+00,  6.5879297e-01,  2.0078125e+00,  7.2153807e-01,\n","         -1.7098346e+00,  2.0389104e-01,  1.6470284e+00, -6.2775612e-02,\n","          3.7644005e-01,  7.6859689e-01, -5.6473649e-01,  2.0389104e-01,\n","          4.7055769e-01, -2.5101089e-01, -1.9921875e+00, -1.5529718e+00,\n","         -1.8980699e+00, -1.0983443e-01, -3.2944226e-01, -1.4120698e-01,\n","          5.9604788e-01, -1.9294424e+00, -1.3804228e+00, -3.0517578e-05,\n","         -8.3140314e-01,  1.4274206e+00, -6.5885413e-01,  1.0195775e+00,\n","          1.1293814e+00,  2.0078125e+00,  1.7568324e+00,  1.0038910e+00,\n","          5.9604788e-01,  2.0078125e+00, -4.8630500e-01, -7.3728549e-01,\n","          2.0078125e+00, -1.4120698e-01, -1.4745405e+00, -9.2552078e-01,\n","         -1.7257953e-01, -1.7257953e-01,  5.6467533e-01, -1.9921875e+00,\n","         -3.1375599e-01, -9.5689332e-01,  1.0038910e+00, -9.8826587e-01,\n","         -1.5716791e-02,  8.1565571e-01, -1.2863052e+00,  1.5683222e-01,\n","         -1.9921875e+00, -1.4902267e+00, -1.9921875e+00, -1.6470895e+00,\n","         -5.6473649e-01, -1.3804228e+00, -1.9451287e+00, -4.5493245e-01,\n","          1.0509501e+00,  2.0078125e+00, -1.5372856e+00,  1.3489892e+00,\n","          2.8232241e-01,  1.7254598e+00,  3.9212632e-01, -1.3647366e+00,\n","         -1.2552071e-01,  1.4587932e+00,  1.4114594e-01,  2.0078125e+00,\n","         -1.5215993e+00, -1.9921875e+00, -1.1921874e+00,  2.1957731e-01,\n","          3.7644005e-01, -1.5716791e-02,  3.1342030e-02,  1.4114594e-01,\n","         -9.5689332e-01, -9.7257960e-01,  5.4898906e-01, -4.7061872e-01,\n","          2.0078125e+00,  1.6156559e+00, -1.8510110e+00, -1.4588542e+00,\n","          6.4310670e-01, -9.0983450e-01, -1.9921875e+00,  3.1342030e-02,\n","         -9.0983450e-01,  1.0509501e+00, -1.3176777e+00,  2.0078125e+00,\n","          1.7097735e+00, -3.1375599e-01,  4.3918514e-01,  1.4744794e+00,\n","          1.4431069e+00, -3.2944226e-01,  1.8038912e+00, -2.9806972e-01,\n","          1.2705579e+00, -2.6669717e-01,  2.0078125e+00,  1.0980089e+00,\n","         -8.9414823e-01,  7.8428316e-01,  8.3134198e-01,  5.6467533e-01,\n","         -3.2944226e-01,  9.5683217e-01,  1.1293814e+00,  2.0078125e+00,\n","         -1.5716791e-02, -8.1571686e-01,  9.2545962e-01, -1.8826580e-01,\n","          6.7447925e-01,  8.1565571e-01,  1.5683222e-01, -2.9806972e-01,\n","          2.1957731e-01, -7.3728549e-01, -4.0787363e-01, -1.9921875e+00,\n","          7.8428316e-01,  3.1342030e-02,  8.3134198e-01,  9.4087124e-02,\n","         -1.9921875e+00, -1.9921875e+00,  1.4114594e-01,  1.1764402e+00,\n","          1.0038910e+00, -1.8826580e-01,  9.8820472e-01,  4.7028303e-02,\n","         -9.0983450e-01, -1.5059130e+00, 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mode, params):\n","  print(\"\\nframe_level_model: features = {}\".format(features)) # features['rgb'].shape = (batch_size, MAX_FRAMES, 1024), features['audio'].shape = (batch_size, MAX_FRAMES, 128)\n","  print(\"frame_level_model: labels = {}\".format(labels))\n","  print(\"frame_level_model: mode = {}\".format(mode))\n","\n","  # 0. Configure network\n","  # Get dynamic batch size\n","  current_batch_size = tf.shape(features['rgb'])[0]\n","  print(\"frame_level_model: current_batch_size = {}\".format(current_batch_size))\n","\n","  # Stack all of the features into a 3-D tensor\n","  combined_features = tf.concat(values = [features['rgb'], features['audio']], axis = 2) # shape = (current_batch_size, MAX_FRAMES, 1024 + 128)\n","  print(\"frame_level_model: combined_features = {}\".format(combined_features))\n","\n","  # Reshape the combined features into a 2-D tensor that we will pass through our DNN\n","  reshaped_combined_features = tf.reshape(tensor = combined_features, shape = [current_batch_size * MAX_FRAMES, 1024 + 128]) # shape = (current_batch_size * MAX_FRAMES, 1024 + 128)\n","  print(\"frame_level_model: reshaped_combined_features = {}\".format(reshaped_combined_features))\n","\n","  # 1. Create the DNN structure now\n","  # Create the input layer to our frame DNN\n","  network = reshaped_combined_features # shape = (current_batch_size * MAX_FRAMES, 1024 + 128)\n","  print(\"frame_level_model: network = reshaped_combined_features = {}\".format(network))\n","\n","  # Add hidden layers with the given number of units/neurons per layer\n","  for units in params['hidden_units']:\n","    network = tf.layers.dense(inputs = network, units = units, activation = tf.nn.relu) # shape = (current_batch_size * MAX_FRAMES, units)\n","    print(\"frame_level_model: network = {}, units = {}\".format(network, units))\n","\n","  # Connect the final hidden layer to a dense layer with no activation to get the logits\n","  logits = tf.layers.dense(inputs = network, units = NUM_CLASSES, activation = None) # shape = (current_batch_size * MAX_FRAMES, NUM_CLASSES)\n","  print(\"frame_level_model: logits = {}\".format(logits))\n","\n","  # Since this is a multi-class, multi-label problem we will apply a sigmoid, not a softmax, to each logit to get its own probability\n","  probabilities = tf.sigmoid(logits) # shape = (current_batch_size * MAX_FRAMES, NUM_CLASSES)\n","  print(\"frame_level_model: probabilities = {}\".format(probabilities))\n","\n","  # Reshape the probabilities back into a 3-D tensor so that we have a frames axis\n","  reshaped_probabilities = tf.reshape(tensor = probabilities, shape = [current_batch_size, MAX_FRAMES, NUM_CLASSES]) # shape = (current_batch_size, MAX_FRAMES, NUM_CLASSES)\n","  print(\"frame_level_model: reshaped_probabilities = {}\".format(reshaped_probabilities))\n","\n","  # Find the average probability over all frames for each label for each example in the batch\n","  average_probabilities_over_frames = tf.reduce_mean(input_tensor = reshaped_probabilities, axis = 1) # shape = (current_batch_size, NUM_CLASSES)\n","  print(\"frame_level_model: average_probabilities_over_frames = {}\".format(average_probabilities_over_frames))\n","\n","  # Select the top k probabilities in descending order\n","  top_k_probabilities = tf.nn.top_k(input = average_probabilities_over_frames, k = params['top_k'], sorted = True) # shape = (current_batch_size, top_k)\n","  print(\"frame_level_model: top_k_probabilities = {}\".format(top_k_probabilities))\n","\n","  # Find the logits from the average probabilities by using the inverse logit \n","  inverse_probabilities_logits = tf.log(average_probabilities_over_frames + 0.00000001) - tf.log(1.0 - average_probabilities_over_frames + 0.00000001) # shape = (current_batch_size, NUM_CLASSES)\n","  print(\"frame_level_model: inverse_probabilities_logits = {}\".format(inverse_probabilities_logits))\n","\n","  # Select the top k logits using the indices of the top k probabilities in descending order\n","  top_k_logits = tf.map_fn(fn = lambda x: tf.gather(params = inverse_probabilities_logits[x], indices = top_k_probabilities.indices[x]), \n","                                elems = tf.range(start = 0, limit = current_batch_size), \n","                                dtype = tf.float32) # shape = (current_batch_size, 1, top_k)\n","  print(\"frame_level_model: top_k_logits = {}\".format(top_k_logits))\n","\n","  # Select the top k classes in descending order of likelihood\n","  top_k_classes = top_k_probabilities.indices # shape = (current_batch_size, top_k)\n","  print(\"frame_level_model: top_k_classes = {}\".format(top_k_classes))\n","\n","  # The 0/1 predictions based on a threshold, in this case the threshold is if the probability it greater than random chance\n","  predictions = tf.where(\n","    condition = average_probabilities_over_frames > 1.0 / NUM_CLASSES, # shape = (current_batch_size, NUM_CLASSES)\n","    x = tf.ones_like(tensor = average_probabilities_over_frames), \n","    y = tf.zeros_like(tensor = average_probabilities_over_frames))\n","  print(\"frame_level_model: predictions = {}\".format(predictions))\n","\n","  # The 0/1 top k predictions based on a threshold, in this case the threshold is if the probability it greater than random chance\n","  top_k_predictions = tf.where(\n","    condition = top_k_probabilities.values > 1.0 / NUM_CLASSES, # shape = (current_batch_size, top_k)\n","    x = tf.ones_like(tensor = top_k_probabilities.values), \n","    y = tf.zeros_like(tensor = top_k_probabilities.values))\n","  print(\"frame_level_model: top_k_predictions = {}\\n\".format(top_k_predictions))\n","\n","  # 2. Loss function, training/eval ops \n","  if mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL:\n","    # Since this is a multi-class, multi-label problem, we will use sigmoid activation and cross entropy loss\n","    # We already have the probabilities we can use the cross entropy formula directly to calculate the loss\n","    loss = tf.reduce_mean(input_tensor = -tf.reduce_sum(input_tensor = labels * tf.log(x = average_probabilities_over_frames + 0.00000001), axis = 1))\n","\n","    train_op = tf.contrib.layers.optimize_loss(\n","      loss = loss,\n","      global_step = tf.train.get_global_step(),\n","      learning_rate = 0.01,\n","      optimizer = \"Adam\")\n","    eval_metric_ops = {\n","        \"accuracy\": tf.metrics.mean_per_class_accuracy(labels = labels, predictions = predictions, num_classes = NUM_CLASSES)\n","    }\n","  else:\n","    loss = None\n","    train_op = None\n","    eval_metric_ops = None\n","\n","  # 3. Create predictions\n","  predictions_dict = {\"logits\": top_k_logits, \n","                      \"probabilities\": top_k_probabilities.values, \n","                      \"predictions\": top_k_predictions,\n","                      \"classes\": top_k_classes}\n","\n","  # 4. Create export outputs\n","  export_outputs = {\"predict_export_outputs\": tf.estimator.export.PredictOutput(outputs = predictions_dict)}\n","\n","  # 5. Return EstimatorSpec\n","  return tf.estimator.EstimatorSpec(\n","    mode = mode,\n","    predictions = predictions_dict,\n","    loss = loss,\n","    train_op = train_op,\n","    eval_metric_ops = eval_metric_ops,\n","    export_outputs = export_outputs)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"W3P9fLrInHFm","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create our serving input function to accept the data at serving and send it in the right format to our custom estimator\n","def serving_input_fn():\n","  # This function fixes the shape and type of our input strings\n","  def fix_shape_and_type_for_serving(placeholder):\n","    # String split each string in the batch and output the values from the resulting SparseTensors\n","    split_string = tf.map_fn(\n","      fn = lambda x: tf.string_split(source = [placeholder[x]], delimiter=',').values, \n","      elems = tf.range(start = 0, limit = tf.shape(input = placeholder)[0]), \n","      dtype = tf.string) # shape = (batch_size, input_sequence_length)\n","    print(\"serving_input_fn: fix_shape_and_type_for_serving: split_string = {}\".format(split_string))\n","\n","    # Convert each string in the split tensor to float\n","    feature_tensor = tf.string_to_number(string_tensor = split_string, out_type = tf.float32) # shape = (batch_size, input_sequence_length)\n","    print(\"serving_input_fn: fix_shape_and_type_for_serving: feature_tensor = {}\".format(feature_tensor))\n","    return feature_tensor\n","  \n","  # This function fixes dynamic shape ambiguity of last dimension so that we will be able to use it in our DNN (since tf.layers.dense require the last dimension to be known)\n","  def get_shape_and_set_modified_shape_3D(tensor, additional_dimension_sizes):\n","    # Get static shape for tensor and convert it to list\n","    shape = tensor.get_shape().as_list()\n","    # Set outer shape to additional_dimension_sizes[0] * additional_dimension_sizes[1] since we know that this is the correct size\n","    shape[1] = additional_dimension_sizes[0] * additional_dimension_sizes[0]\n","    # Set the shape of tensor to our modified shape\n","    tensor.set_shape(shape = shape) # shape = (batch_size, additional_dimension_sizes[0] * additional_dimension_sizes[1])\n","    print(\"serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = {}, additional_dimension_sizes = {}\".format(tensor, additional_dimension_sizes))\n","    # Finally reshape tensor into the shape it is supposed to be for the model function\n","    tensor = tf.reshape(tensor = tensor, shape = [-1, additional_dimension_sizes[0], additional_dimension_sizes[1]]) # shape = (batch_size, additional_dimension_sizes[0], additional_dimension_sizes[1])\n","    print(\"serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = {}, additional_dimension_sizes = {}\".format(tensor, additional_dimension_sizes))\n","    return tensor\n","  \n","  # Create placeholders to accept the data sent to the model at serving time\n","  feature_placeholders = { # all features come in as a batch of strings, shape = (batch_size,), this was so because of passing the arrays to online ml-engine prediction\n","    'video_id': tf.placeholder(dtype = tf.string, shape = [None]),\n","    'rgb': tf.placeholder(dtype = tf.string, shape = [None]),\n","    'audio': tf.placeholder(dtype = tf.string, shape = [None])\n","  }\n","  print(\"\\nserving_input_fn: feature_placeholders = {}\".format(feature_placeholders))\n","\n","  # Create feature tensors\n","  features = {\n","    \"video_id\": feature_placeholders[\"video_id\"],\n","    \"rgb\": fix_shape_and_type_for_serving(placeholder = feature_placeholders[\"rgb\"]),\n","    \"audio\": fix_shape_and_type_for_serving(placeholder = feature_placeholders[\"audio\"])\n","  }\n","  print(\"serving_input_fn: features = {}\".format(features))\n","\n","  # Fix dynamic shape ambiguity of feature tensors for our DNN\n","  features[\"rgb\"] = get_shape_and_set_modified_shape_3D(tensor = features[\"rgb\"], additional_dimension_sizes = [MAX_FRAMES, 1024])\n","  features[\"audio\"] = get_shape_and_set_modified_shape_3D(tensor = features[\"audio\"], additional_dimension_sizes = [MAX_FRAMES, 128])\n","  print(\"serving_input_fn: features = {}\\n\".format(features))\n","  \n","  return tf.estimator.export.ServingInputReceiver(features = features, receiver_tensors = feature_placeholders)"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"IdjHD0C7nHFp","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create custom estimator's train and evaluate function\n","def train_and_evaluate(args):\n","  # Create our custome estimator using our model function\n","  estimator = tf.estimator.Estimator(\n","    model_fn = frame_level_model, \n","    model_dir = args['output_dir'],\n","    params = {'hidden_units': args['hidden_units'], 'top_k': args['top_k']})\n","  # Create train spec to read in our training data\n","  train_spec = tf.estimator.TrainSpec(\n","    input_fn = read_dataset_frame(\n","      file_pattern = args['train_file_pattern'], \n","      mode = tf.estimator.ModeKeys.TRAIN, \n","      batch_size = args['batch_size']),\n","    max_steps = args['train_steps'])\n","  # Create exporter to save out the complete model to disk\n","  exporter = tf.estimator.LatestExporter(name = 'exporter', serving_input_receiver_fn = serving_input_fn)\n","  # Create eval spec to read in our validation data and export our model\n","  eval_spec = tf.estimator.EvalSpec(\n","    input_fn = read_dataset_frame(\n","      file_pattern = args['eval_file_pattern'], \n","      mode = tf.estimator.ModeKeys.EVAL, \n","      batch_size = args['batch_size']),\n","    steps = None,\n","    exporters = exporter,\n","    start_delay_secs = args['start_delay_secs'],\n","    throttle_secs = args['throttle_secs'])\n","  # Create train and evaluate loop to train and evaluate our estimator\n","  tf.estimator.train_and_evaluate(estimator = estimator, train_spec = train_spec, eval_spec = eval_spec)"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"ysxfXUUFnHFr","colab_type":"code","colab":{},"outputId":"7720f953-409d-4af1-a7ad-a753de80a755"},"cell_type":"code","source":["# Run the training job\n","shutil.rmtree(arguments['output_dir'], ignore_errors = True) # start fresh each time\n","train_and_evaluate(args = arguments)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["INFO:tensorflow:Using default config.\n","INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fc22b1f1690>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': 'trained_model', '_global_id_in_cluster': 0, '_save_summary_steps': 100}\n","INFO:tensorflow:Running training and evaluation locally (non-distributed).\n","INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 30 secs (eval_spec.throttle_secs) or training is finished.\n","\n","read_dataset_frame: _input_fn: file_pattern = gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\n","read_dataset_frame: _input_fn: mode = train\n","read_dataset_frame: _input_fn: batch_size = 10\n","read_dataset_frame: _input_fn: dataset.TFRecordDataset = <TFRecordDataset shapes: (), types: tf.string>\n","\n","read_dataset_frame: _input_fn: decode_example: contexts = {'labels': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fc218198a50>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_rgb = Tensor(\"Reshape:0\", shape=(?, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: rgb_matrix = Tensor(\"concat:0\", shape=(20, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_audio = Tensor(\"Reshape_1:0\", shape=(?, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: audio_matrix = Tensor(\"concat_1:0\", shape=(20, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'concat_1:0' shape=(20, 128) dtype=float32>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: labels = Tensor(\"Cast_4:0\", shape=(4716,), dtype=float32)\n","\n","read_dataset_frame: _input_fn: dataset.map = <MapDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.shuffle = <ShuffleDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.repeat = <RepeatDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.batch = <BatchDataset shapes: ({rgb: (?, 20, 1024), audio: (?, 20, 128), video_id: (?,)}, (?, 4716)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: batch_features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: batch_labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","\n","INFO:tensorflow:Calling model_fn.\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","frame_level_model: labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","frame_level_model: mode = train\n","frame_level_model: current_batch_size = Tensor(\"strided_slice:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 64), dtype=float32), units = 64\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_1:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Create CheckpointSaverHook.\n","INFO:tensorflow:Graph was finalized.\n","INFO:tensorflow:Running local_init_op.\n","INFO:tensorflow:Done running local_init_op.\n","INFO:tensorflow:Saving checkpoints for 1 into trained_model/model.ckpt.\n","INFO:tensorflow:loss = 2.6184533, step = 1\n","INFO:tensorflow:Saving checkpoints for 100 into trained_model/model.ckpt.\n","INFO:tensorflow:Loss for final step: 0.2414387.\n","\n","read_dataset_frame: _input_fn: file_pattern = gs://youtube-8m-team/1/frame_level/validate/validate-0.tfrecord\n","read_dataset_frame: _input_fn: mode = eval\n","read_dataset_frame: _input_fn: batch_size = 10\n","read_dataset_frame: _input_fn: dataset.TFRecordDataset = <TFRecordDataset shapes: (), types: tf.string>\n","\n","read_dataset_frame: _input_fn: decode_example: contexts = {'labels': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fc21afa8250>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_rgb = Tensor(\"Reshape:0\", shape=(?, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: rgb_matrix = Tensor(\"concat:0\", shape=(20, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_audio = Tensor(\"Reshape_1:0\", shape=(?, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: audio_matrix = Tensor(\"concat_1:0\", shape=(20, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'concat_1:0' shape=(20, 128) dtype=float32>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: labels = Tensor(\"Cast_4:0\", shape=(4716,), dtype=float32)\n","\n","read_dataset_frame: _input_fn: dataset.map = <MapDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.repeat = <RepeatDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.batch = <BatchDataset shapes: ({rgb: (?, 20, 1024), audio: (?, 20, 128), video_id: (?,)}, (?, 4716)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: batch_features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: batch_labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","\n","INFO:tensorflow:Calling model_fn.\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","frame_level_model: labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","frame_level_model: mode = eval\n","frame_level_model: current_batch_size = Tensor(\"strided_slice:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 64), dtype=float32), units = 64\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_1:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Starting evaluation at 2018-05-25-05:29:48\n","INFO:tensorflow:Graph was finalized.\n","INFO:tensorflow:Restoring parameters from trained_model/model.ckpt-100\n","INFO:tensorflow:Running local_init_op.\n","INFO:tensorflow:Done running local_init_op.\n","INFO:tensorflow:Finished evaluation at 2018-05-25-05:29:51\n","INFO:tensorflow:Saving dict for global step 100: accuracy = 0.0002120441, global_step = 100, loss = 0.47365227\n","\n","serving_input_fn: feature_placeholders = {'rgb': <tf.Tensor 'Placeholder_1:0' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Placeholder_2:0' shape=(?,) dtype=string>}\n","serving_input_fn: fix_shape_and_type_for_serving: split_string = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, ?), dtype=string)\n","serving_input_fn: fix_shape_and_type_for_serving: feature_tensor = Tensor(\"StringToNumber:0\", shape=(?, ?), dtype=float32)\n","serving_input_fn: fix_shape_and_type_for_serving: split_string = Tensor(\"map_1/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, ?), dtype=string)\n","serving_input_fn: fix_shape_and_type_for_serving: feature_tensor = Tensor(\"StringToNumber_1:0\", shape=(?, ?), dtype=float32)\n","serving_input_fn: features = {'rgb': <tf.Tensor 'StringToNumber:0' shape=(?, ?) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'StringToNumber_1:0' shape=(?, ?) dtype=float32>}\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"StringToNumber:0\", shape=(?, 400), dtype=float32), additional_dimension_sizes = [20, 1024]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"Reshape:0\", shape=(?, 20, 1024), dtype=float32), additional_dimension_sizes = [20, 1024]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"StringToNumber_1:0\", shape=(?, 400), dtype=float32), additional_dimension_sizes = [20, 128]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"Reshape_1:0\", shape=(?, 20, 128), dtype=float32), additional_dimension_sizes = [20, 128]\n","serving_input_fn: features = {'rgb': <tf.Tensor 'Reshape:0' shape=(?, 20, 1024) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Reshape_1:0' shape=(?, 20, 128) dtype=float32>}\n","\n","INFO:tensorflow:Calling model_fn.\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'Reshape:0' shape=(?, 20, 1024) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Reshape_1:0' shape=(?, 20, 128) dtype=float32>}\n","frame_level_model: labels = None\n","frame_level_model: mode = infer\n","frame_level_model: current_batch_size = Tensor(\"strided_slice_2:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape_2:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape_2:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 64), dtype=float32), units = 64\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_3:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map_2/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Signatures INCLUDED in export for Classify: None\n","INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n","INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default', 'predict_export_outputs']\n","INFO:tensorflow:Restoring parameters from trained_model/model.ckpt-100\n","INFO:tensorflow:Assets added to graph.\n","INFO:tensorflow:No assets to write.\n","INFO:tensorflow:SavedModel written to: trained_model/export/exporter/temp-1527226192/saved_model.pb\n"],"name":"stdout"}]},{"metadata":{"deletable":true,"editable":true,"id":"_uIl4ssJnHFu","colab_type":"text"},"cell_type":"markdown","source":["# Training"]},{"metadata":{"deletable":true,"editable":true,"id":"8LndPM28nHFv","colab_type":"text"},"cell_type":"markdown","source":["### Locally"]},{"metadata":{"deletable":true,"editable":true,"id":"4EWkvOkmnHFv","colab_type":"code","colab":{},"outputId":"7c36ed09-9974-491d-9dc2-8dec46776ea2"},"cell_type":"code","source":["%bash\n","OUTDIR=trained_model\n","rm -rf $OUTDIR\n","export PYTHONPATH=$PYTHONPATH:$PWD/trainer\n","python -m trainer.task \\\n","  --train_file_pattern=\"gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\" \\\n","  --eval_file_pattern=\"gs://youtube-8m-team/1/frame_level/validate/validate-0.tfrecord\"  \\\n","  --output_dir=$OUTDIR \\\n","  --batch_size=10 \\\n","  --train_steps=100 \\\n","  --hidden_units=\"1024 512 256\" \\\n","  --top_k=5 \\\n","  --job-dir=./tmp"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\n","read_dataset_frame: _input_fn: file_pattern = gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\n","read_dataset_frame: _input_fn: mode = train\n","read_dataset_frame: _input_fn: batch_size = 10\n","read_dataset_frame: _input_fn: dataset.TFRecordDataset = <TFRecordDataset shapes: (), types: tf.string>\n","read_dataset_frame: _input_fn: decode_example: contexts = {'labels': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fae90d85e90>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_rgb = Tensor(\"Reshape:0\", shape=(?, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: rgb_matrix = Tensor(\"concat:0\", shape=(20, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_audio = Tensor(\"Reshape_1:0\", shape=(?, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: audio_matrix = Tensor(\"concat_1:0\", shape=(20, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'concat_1:0' shape=(20, 128) dtype=float32>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: labels = Tensor(\"Cast_4:0\", shape=(4716,), dtype=float32)\n","\n","read_dataset_frame: _input_fn: dataset.map = <MapDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.shuffle = <ShuffleDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.repeat = <RepeatDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.batch = <BatchDataset shapes: ({rgb: (?, 20, 1024), audio: (?, 20, 128), video_id: (?,)}, (?, 4716)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: batch_features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: batch_labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","frame_level_model: labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","frame_level_model: mode = train\n","frame_level_model: current_batch_size = Tensor(\"strided_slice:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 512), dtype=float32), units = 512\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_1:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n","\n","read_dataset_frame: _input_fn: file_pattern = gs://youtube-8m-team/1/frame_level/validate/validate-0.tfrecord\n","read_dataset_frame: _input_fn: mode = eval\n","read_dataset_frame: _input_fn: batch_size = 10\n","read_dataset_frame: _input_fn: dataset.TFRecordDataset = <TFRecordDataset shapes: (), types: tf.string>\n","read_dataset_frame: _input_fn: decode_example: contexts = {'labels': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fae7e047c10>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_rgb = Tensor(\"Reshape:0\", shape=(?, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: rgb_matrix = Tensor(\"concat:0\", shape=(20, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_audio = Tensor(\"Reshape_1:0\", shape=(?, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: audio_matrix = Tensor(\"concat_1:0\", shape=(20, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'concat_1:0' shape=(20, 128) dtype=float32>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: labels = Tensor(\"Cast_4:0\", shape=(4716,), dtype=float32)\n","\n","read_dataset_frame: _input_fn: dataset.map = <MapDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.repeat = <RepeatDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.batch = <BatchDataset shapes: ({rgb: (?, 20, 1024), audio: (?, 20, 128), video_id: (?,)}, (?, 4716)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: batch_features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: batch_labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","frame_level_model: labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","frame_level_model: mode = eval\n","frame_level_model: current_batch_size = Tensor(\"strided_slice:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 512), dtype=float32), units = 512\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_1:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n","\n","serving_input_fn: feature_placeholders = {'rgb': <tf.Tensor 'Placeholder_1:0' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Placeholder_2:0' shape=(?,) dtype=string>}\n","serving_input_fn: fix_shape_and_type_for_serving: split_string = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, ?), dtype=string)\n","serving_input_fn: fix_shape_and_type_for_serving: feature_tensor = Tensor(\"StringToNumber:0\", shape=(?, ?), dtype=float32)\n","serving_input_fn: fix_shape_and_type_for_serving: split_string = Tensor(\"map_1/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, ?), dtype=string)\n","serving_input_fn: fix_shape_and_type_for_serving: feature_tensor = Tensor(\"StringToNumber_1:0\", shape=(?, ?), dtype=float32)\n","serving_input_fn: features = {'rgb': <tf.Tensor 'StringToNumber:0' shape=(?, ?) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'StringToNumber_1:0' shape=(?, ?) dtype=float32>}\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"StringToNumber:0\", shape=(?, 400), dtype=float32), additional_dimension_sizes = [20, 1024]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"Reshape:0\", shape=(?, 20, 1024), dtype=float32), additional_dimension_sizes = [20, 1024]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"StringToNumber_1:0\", shape=(?, 400), dtype=float32), additional_dimension_sizes = [20, 128]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"Reshape_1:0\", shape=(?, 20, 128), dtype=float32), additional_dimension_sizes = [20, 128]\n","serving_input_fn: features = {'rgb': <tf.Tensor 'Reshape:0' shape=(?, 20, 1024) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Reshape_1:0' shape=(?, 20, 128) dtype=float32>}\n","\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'Reshape:0' shape=(?, 20, 1024) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Reshape_1:0' shape=(?, 20, 128) dtype=float32>}\n","frame_level_model: labels = None\n","frame_level_model: mode = infer\n","frame_level_model: current_batch_size = Tensor(\"strided_slice_2:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape_2:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape_2:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 512), dtype=float32), units = 512\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_3:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map_2/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n","\n","read_dataset_frame: _input_fn: file_pattern = gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\n","read_dataset_frame: _input_fn: mode = train\n","read_dataset_frame: _input_fn: batch_size = 10\n","read_dataset_frame: _input_fn: dataset.TFRecordDataset = <TFRecordDataset shapes: (), types: tf.string>\n","read_dataset_frame: _input_fn: decode_example: contexts = {'labels': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fae7e2bb550>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_rgb = Tensor(\"Reshape:0\", shape=(?, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: rgb_matrix = Tensor(\"concat:0\", shape=(20, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_audio = Tensor(\"Reshape_1:0\", shape=(?, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: audio_matrix = Tensor(\"concat_1:0\", shape=(20, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'concat_1:0' shape=(20, 128) dtype=float32>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: labels = Tensor(\"Cast_4:0\", shape=(4716,), dtype=float32)\n","\n","read_dataset_frame: _input_fn: dataset.map = <MapDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.shuffle = <ShuffleDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.repeat = <RepeatDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.batch = <BatchDataset shapes: ({rgb: (?, 20, 1024), audio: (?, 20, 128), video_id: (?,)}, (?, 4716)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: batch_features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: batch_labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","frame_level_model: labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","frame_level_model: mode = train\n","frame_level_model: current_batch_size = Tensor(\"strided_slice:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 512), dtype=float32), units = 512\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_1:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n","\n","read_dataset_frame: _input_fn: file_pattern = gs://youtube-8m-team/1/frame_level/validate/validate-0.tfrecord\n","read_dataset_frame: _input_fn: mode = eval\n","read_dataset_frame: _input_fn: batch_size = 10\n","read_dataset_frame: _input_fn: dataset.TFRecordDataset = <TFRecordDataset shapes: (), types: tf.string>\n","read_dataset_frame: _input_fn: decode_example: contexts = {'labels': <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fae7e1eb510>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:5' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_rgb = Tensor(\"Reshape:0\", shape=(?, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: rgb_matrix = Tensor(\"concat:0\", shape=(20, 1024), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:4' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: decoded_audio = Tensor(\"Reshape_1:0\", shape=(?, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: audio_matrix = Tensor(\"concat_1:0\", shape=(20, 128), dtype=float32)\n","read_dataset_frame: _input_fn: decode_example: features = {'rgb': <tf.Tensor 'concat:0' shape=(20, 1024) dtype=float32>, 'audio': <tf.Tensor 'concat_1:0' shape=(20, 128) dtype=float32>, 'video_id': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:3' shape=() dtype=string>}\n","read_dataset_frame: _input_fn: decode_example: labels = Tensor(\"Cast_4:0\", shape=(4716,), dtype=float32)\n","\n","read_dataset_frame: _input_fn: dataset.map = <MapDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.repeat = <RepeatDataset shapes: ({rgb: (20, 1024), audio: (20, 128), video_id: ()}, (4716,)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: dataset.batch = <BatchDataset shapes: ({rgb: (?, 20, 1024), audio: (?, 20, 128), video_id: (?,)}, (?, 4716)), types: ({rgb: tf.float32, audio: tf.float32, video_id: tf.string}, tf.float32)>\n","read_dataset_frame: _input_fn: batch_features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","read_dataset_frame: _input_fn: batch_labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'IteratorGetNext:1' shape=(?, 20, 1024) dtype=float32>, 'audio': <tf.Tensor 'IteratorGetNext:0' shape=(?, 20, 128) dtype=float32>, 'video_id': <tf.Tensor 'IteratorGetNext:2' shape=(?,) dtype=string>}\n","frame_level_model: labels = Tensor(\"IteratorGetNext:3\", shape=(?, 4716), dtype=float32, device=/device:CPU:0)\n","frame_level_model: mode = eval\n","frame_level_model: current_batch_size = Tensor(\"strided_slice:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 512), dtype=float32), units = 512\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_1:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n","\n","serving_input_fn: feature_placeholders = {'rgb': <tf.Tensor 'Placeholder_1:0' shape=(?,) dtype=string>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Placeholder_2:0' shape=(?,) dtype=string>}\n","serving_input_fn: fix_shape_and_type_for_serving: split_string = Tensor(\"map/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, ?), dtype=string)\n","serving_input_fn: fix_shape_and_type_for_serving: feature_tensor = Tensor(\"StringToNumber:0\", shape=(?, ?), dtype=float32)\n","serving_input_fn: fix_shape_and_type_for_serving: split_string = Tensor(\"map_1/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, ?), dtype=string)\n","serving_input_fn: fix_shape_and_type_for_serving: feature_tensor = Tensor(\"StringToNumber_1:0\", shape=(?, ?), dtype=float32)\n","serving_input_fn: features = {'rgb': <tf.Tensor 'StringToNumber:0' shape=(?, ?) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'StringToNumber_1:0' shape=(?, ?) dtype=float32>}\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"StringToNumber:0\", shape=(?, 400), dtype=float32), additional_dimension_sizes = [20, 1024]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"Reshape:0\", shape=(?, 20, 1024), dtype=float32), additional_dimension_sizes = [20, 1024]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"StringToNumber_1:0\", shape=(?, 400), dtype=float32), additional_dimension_sizes = [20, 128]\n","serving_input_fn: get_shape_and_set_modified_shape_3D: tensor = Tensor(\"Reshape_1:0\", shape=(?, 20, 128), dtype=float32), additional_dimension_sizes = [20, 128]\n","serving_input_fn: features = {'rgb': <tf.Tensor 'Reshape:0' shape=(?, 20, 1024) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Reshape_1:0' shape=(?, 20, 128) dtype=float32>}\n","\n","\n","frame_level_model: features = {'rgb': <tf.Tensor 'Reshape:0' shape=(?, 20, 1024) dtype=float32>, 'video_id': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=string>, 'audio': <tf.Tensor 'Reshape_1:0' shape=(?, 20, 128) dtype=float32>}\n","frame_level_model: labels = None\n","frame_level_model: mode = infer\n","frame_level_model: current_batch_size = Tensor(\"strided_slice_2:0\", shape=(), dtype=int32)\n","frame_level_model: combined_features = Tensor(\"concat:0\", shape=(?, 20, 1152), dtype=float32)\n","frame_level_model: reshaped_combined_features = Tensor(\"Reshape_2:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = reshaped_combined_features = Tensor(\"Reshape_2:0\", shape=(?, 1152), dtype=float32)\n","frame_level_model: network = Tensor(\"dense/Relu:0\", shape=(?, 1024), dtype=float32), units = 1024\n","frame_level_model: network = Tensor(\"dense_1/Relu:0\", shape=(?, 512), dtype=float32), units = 512\n","frame_level_model: network = Tensor(\"dense_2/Relu:0\", shape=(?, 256), dtype=float32), units = 256\n","frame_level_model: logits = Tensor(\"dense_3/BiasAdd:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: probabilities = Tensor(\"Sigmoid:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: reshaped_probabilities = Tensor(\"Reshape_3:0\", shape=(?, 20, 4716), dtype=float32)\n","frame_level_model: average_probabilities_over_frames = Tensor(\"Mean:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_probabilities = TopKV2(values=<tf.Tensor 'TopKV2:0' shape=(?, 5) dtype=float32>, indices=<tf.Tensor 'TopKV2:1' shape=(?, 5) dtype=int32>)\n","frame_level_model: inverse_probabilities_logits = Tensor(\"sub_1:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_logits = Tensor(\"map_2/TensorArrayStack/TensorArrayGatherV3:0\", shape=(?, 5), dtype=float32)\n","frame_level_model: top_k_classes = Tensor(\"TopKV2:1\", shape=(?, 5), dtype=int32)\n","frame_level_model: predictions = Tensor(\"Select:0\", shape=(?, 4716), dtype=float32)\n","frame_level_model: top_k_predictions = Tensor(\"Select_1:0\", shape=(?, 5), dtype=float32)\n","\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/envs/py2env/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n","  from ._conv import register_converters as _register_converters\n","INFO:tensorflow:Using default config.\n","INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fae91e8afd0>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': 'trained_model/', '_global_id_in_cluster': 0, '_save_summary_steps': 100}\n","INFO:tensorflow:Running training and evaluation locally (non-distributed).\n","INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 30 secs (eval_spec.throttle_secs) or training is finished.\n","INFO:tensorflow:Calling model_fn.\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Create CheckpointSaverHook.\n","INFO:tensorflow:Graph was finalized.\n","2018-05-25 05:14:17.517722: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n","INFO:tensorflow:Running local_init_op.\n","INFO:tensorflow:Done running local_init_op.\n","INFO:tensorflow:Saving checkpoints for 1 into trained_model/model.ckpt.\n","INFO:tensorflow:loss = 3.6297615, step = 1\n","INFO:tensorflow:Saving checkpoints for 91 into trained_model/model.ckpt.\n","INFO:tensorflow:Loss for final step: 0.21205425.\n","INFO:tensorflow:Calling model_fn.\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Starting evaluation at 2018-05-25-05:14:53\n","INFO:tensorflow:Graph was finalized.\n","INFO:tensorflow:Restoring parameters from trained_model/model.ckpt-91\n","INFO:tensorflow:Running local_init_op.\n","INFO:tensorflow:Done running local_init_op.\n","INFO:tensorflow:Finished evaluation at 2018-05-25-05:15:03\n","INFO:tensorflow:Saving dict for global step 91: accuracy = 0.00021206953, global_step = 91, loss = 0.5385165\n","INFO:tensorflow:Calling model_fn.\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Signatures INCLUDED in export for Classify: None\n","INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n","INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default', 'predict_export_outputs']\n","INFO:tensorflow:Restoring parameters from trained_model/model.ckpt-91\n","INFO:tensorflow:Assets added to graph.\n","INFO:tensorflow:No assets to write.\n","INFO:tensorflow:SavedModel written to: trained_model/export/exporter/temp-1527225305/saved_model.pb\n","INFO:tensorflow:Calling model_fn.\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Create CheckpointSaverHook.\n","INFO:tensorflow:Graph was finalized.\n","INFO:tensorflow:Restoring parameters from trained_model/model.ckpt-91\n","INFO:tensorflow:Running local_init_op.\n","INFO:tensorflow:Done running local_init_op.\n","INFO:tensorflow:Saving checkpoints for 92 into trained_model/model.ckpt.\n","INFO:tensorflow:loss = 0.00021824069, step = 92\n","INFO:tensorflow:Saving checkpoints for 100 into trained_model/model.ckpt.\n","INFO:tensorflow:Loss for final step: 0.00024316735.\n","INFO:tensorflow:Calling model_fn.\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Starting evaluation at 2018-05-25-05:15:18\n","INFO:tensorflow:Graph was finalized.\n","INFO:tensorflow:Restoring parameters from trained_model/model.ckpt-100\n","INFO:tensorflow:Running local_init_op.\n","INFO:tensorflow:Done running local_init_op.\n","INFO:tensorflow:Finished evaluation at 2018-05-25-05:15:24\n","INFO:tensorflow:Saving dict for global step 100: accuracy = 0.00021205972, global_step = 100, loss = 0.52153826\n","INFO:tensorflow:Calling model_fn.\n","INFO:tensorflow:Done calling model_fn.\n","INFO:tensorflow:Signatures INCLUDED in export for Classify: None\n","INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n","INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default', 'predict_export_outputs']\n","INFO:tensorflow:Restoring parameters from trained_model/model.ckpt-100\n","INFO:tensorflow:Assets added to graph.\n","INFO:tensorflow:No assets to write.\n","INFO:tensorflow:SavedModel written to: trained_model/export/exporter/temp-1527225325/saved_model.pb\n"],"name":"stderr"}]},{"metadata":{"deletable":true,"editable":true,"id":"HHv-mYg1nHFy","colab_type":"text"},"cell_type":"markdown","source":["### GCloud"]},{"metadata":{"deletable":true,"editable":true,"id":"B_2Bh2FknHFz","colab_type":"code","colab":{},"outputId":"96244081-920d-446b-a5eb-7a669f05b96c"},"cell_type":"code","source":["%bash\n","OUTDIR=gs://$BUCKET/youtube_8m_frame_level_datasets/trained_model\n","JOBNAME=job_youtube_8m_frame_level_datasets$(date -u +%y%m%d_%H%M%S)\n","echo $OUTDIR $REGION $JOBNAME\n","gsutil -m rm -rf $OUTDIR\n","gcloud ml-engine jobs submit training $JOBNAME \\\n","  --region=$REGION \\\n","  --module-name=trainer.task \\\n","  --package-path=$PWD/trainer \\\n","  --job-dir=$OUTDIR \\\n","  --staging-bucket=gs://$BUCKET \\\n","  --scale-tier=STANDARD_1 \\\n","  --runtime-version=1.5 \\\n","  -- \\\n","  --train_file_pattern=\"gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\" \\\n","  --eval_file_pattern=\"gs://youtube-8m-team/1/frame_level/validate/validate-0.tfrecord\"  \\\n","  --output_dir=$OUTDIR \\\n","  --batch_size=50 \\\n","  --train_steps=10000 \\\n","  --hidden_units=\"1024 512 256\" \\\n","  --top_k=5 \\\n","  --job-dir=$OUTDIR"],"execution_count":0,"outputs":[{"output_type":"stream","text":["gs://youtube8m-4-train/youtube_8m_frame_level_datasets/trained_model us-central1 job_youtube_8m_frame_level_datasets180525_053143\n","jobId: job_youtube_8m_frame_level_datasets180525_053143\n","state: QUEUED\n"],"name":"stdout"},{"output_type":"stream","text":["CommandException: 1 files/objects could not be removed.\n","Job [job_youtube_8m_frame_level_datasets180525_053143] submitted successfully.\n","Your job is still active. You may view the status of your job with the command\n","\n","  $ gcloud ml-engine jobs describe job_youtube_8m_frame_level_datasets180525_053143\n","\n","or continue streaming the logs with the command\n","\n","  $ gcloud ml-engine jobs stream-logs job_youtube_8m_frame_level_datasets180525_053143\n"],"name":"stderr"}]},{"metadata":{"id":"7IihR79vnHF3","colab_type":"text"},"cell_type":"markdown","source":["### Hyperparameter tuning"]},{"metadata":{"id":"DLNC7cAmnHF3","colab_type":"code","colab":{},"outputId":"b1d861f3-6ceb-455c-ed5e-ae78e127e575"},"cell_type":"code","source":["%writefile hyperparam.yaml\n","trainingInput:\n","  scaleTier: STANDARD_1\n","  hyperparameters:\n","    hyperparameterMetricTag: accuracy\n","    goal: MAXIMIZE\n","    maxTrials: 30\n","    maxParallelTrials: 1\n","    params:\n","    - parameterName: batch_size\n","      type: INTEGER\n","      minValue: 8\n","      maxValue: 512\n","      scaleType: UNIT_LOG_SCALE\n","    - parameterName: hidden_units\n","      type: CATEGORICAL\n","      categoricalValues: [\"64 32\", \"256 128 16\", \"64 64 64 8\"]"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Writing hyperparam.yaml\n"],"name":"stdout"}]},{"metadata":{"id":"M2D3ucc_nHF7","colab_type":"code","colab":{},"outputId":"c23f0d19-9c6a-493a-895d-6cce7c92c89f"},"cell_type":"code","source":["%bash\n","OUTDIR=gs://$BUCKET/youtube_8m_frame_level_datasets/hyperparam\n","JOBNAME=job_youtube_8m_frame_level_datasets$(date -u +%y%m%d_%H%M%S)\n","echo $OUTDIR $REGION $JOBNAME\n","gsutil -m rm -rf $OUTDIR\n","gcloud ml-engine jobs submit training $JOBNAME \\\n","  --region=$REGION \\\n","  --module-name=trainer.task \\\n","  --package-path=$PWD/trainer \\\n","  --job-dir=$OUTDIR \\\n","  --staging-bucket=gs://$BUCKET \\\n","  --scale-tier=STANDARD_1 \\\n","  --config=hyperparam.yaml \\\n","  --runtime-version=1.5 \\\n","  -- \\\n","  --train_file_pattern=\"gs://youtube-8m-team/1/frame_level/train/train*.tfrecord\" \\\n","  --eval_file_pattern=\"gs://youtube-8m-team/1/frame_level/validate/validate-0.tfrecord\"  \\\n","  --output_dir=$OUTDIR \\\n","  --train_steps=10000 \\\n","  --top_k=5 \\\n","  --job-dir=$OUTDIR"],"execution_count":0,"outputs":[{"output_type":"stream","text":["gs://youtube8m-4-train/youtube_8m_frame_level_datasets/hyperparam us-central1 job_youtube_8m_frame_level_datasets180525_061627\n","jobId: job_youtube_8m_frame_level_datasets180525_061627\n","state: QUEUED\n"],"name":"stdout"},{"output_type":"stream","text":["CommandException: 1 files/objects could not be removed.\n","Job [job_youtube_8m_frame_level_datasets180525_061627] submitted successfully.\n","Your job is still active. You may view the status of your job with the command\n","\n","  $ gcloud ml-engine jobs describe job_youtube_8m_frame_level_datasets180525_061627\n","\n","or continue streaming the logs with the command\n","\n","  $ gcloud ml-engine jobs stream-logs job_youtube_8m_frame_level_datasets180525_061627\n"],"name":"stderr"}]},{"metadata":{"deletable":true,"editable":true,"id":"1tapVu9BnHF-","colab_type":"text"},"cell_type":"markdown","source":["# Deploy"]},{"metadata":{"deletable":true,"editable":true,"id":"Dg5O3CJ1nHF_","colab_type":"code","colab":{},"outputId":"10c918c5-b8ca-44c7-bf9f-6271c671eb8f"},"cell_type":"code","source":["%bash\n","MODEL_NAME=\"youtube_8m_frame_level_datasets\"\n","MODEL_VERSION=\"v1\"\n","MODEL_LOCATION=$(gsutil ls gs://$BUCKET/youtube_8m_frame_level_datasets/trained_model/export/exporter/ | tail -1)\n","echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n","#gcloud ml-engine versions delete ${MODEL_VERSION} --model ${MODEL_NAME}\n","#gcloud ml-engine models delete ${MODEL_NAME}\n","gcloud ml-engine models create $MODEL_NAME --regions $REGION\n","gcloud ml-engine versions create $MODEL_VERSION --model $MODEL_NAME --origin $MODEL_LOCATION --runtime-version 1.5"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Deleting and deploying youtube_8m_frame_level_datasets v1 from gs://youtube8m-4-train/youtube_8m_frame_level_datasets/trained_model/export/exporter/1527219410/ ... this will take a few minutes\n"],"name":"stdout"},{"output_type":"stream","text":["Created ml engine model [projects/qwiklabs-gcp-8d3d0cd07cef9252/models/youtube_8m_frame_level_datasets].\n","Creating version (this might take a few minutes)......\n",".............................................................................................................done.\n"],"name":"stderr"}]},{"metadata":{"deletable":true,"editable":true,"id":"RrQOIljFnHGD","colab_type":"text"},"cell_type":"markdown","source":["# Prediction"]},{"metadata":{"deletable":true,"editable":true,"id":"u1tk2adWnHGD","colab_type":"text"},"cell_type":"markdown","source":["### Prep"]},{"metadata":{"deletable":true,"editable":true,"id":"lPc-RH0WnHGG","colab_type":"code","colab":{}},"cell_type":"code","source":["# Let's call our input function to decode our data to put into BigQuery for testing predictions\n","video_list = []\n","with tf.Session() as sess:\n","  fn = read_dataset_frame(\n","    file_pattern = \"gs://youtube-8m-team/1/frame_level/validate/validate-0.tfrecord\", \n","    mode = tf.estimator.ModeKeys.EVAL, \n","    batch_size = 1)\n","  batch_features, batch_labels = fn()\n","  for key,value in batch_features.items():\n","    batch_features[key] = tf.squeeze(batch_features[key])\n","  fixed_batch_features = batch_features\n","  fixed_batch_labels = tf.squeeze(batch_labels)\n","\n","  while True:\n","    features, labels = sess.run([fixed_batch_features, fixed_batch_labels])\n","    features[\"labels\"] = labels\n","    video_list.append(features)"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"DRZoXObRnHGI","colab_type":"code","colab":{},"outputId":"e96893f5-21cc-4438-e20f-e38591a75f7e"},"cell_type":"code","source":["# This is the number of videos from the frame level file we just processed\n","len(video_list)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["331"]},"metadata":{"tags":[]},"execution_count":31}]},{"metadata":{"deletable":true,"editable":true,"id":"GLEr1xuInHGM","colab_type":"code","colab":{}},"cell_type":"code","source":["# Convert the nd-arrays to lists and cast to strings (video_id is already a single string)\n","for items in video_list:\n","  items[\"rgb\"] = str(items[\"rgb\"].tolist())\n","  items[\"audio\"] = str(items[\"audio\"].tolist())\n","  items[\"labels\"] = str(items[\"labels\"].tolist())"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"UqYdJPN_nHGO","colab_type":"code","colab":{},"outputId":"b2c14939-5360-4e1b-96c3-5e82bc851740"},"cell_type":"code","source":["# Create a dataframe from the list\n","video_df = pd.DataFrame(video_list)\n","video_df = video_df[[\"video_id\", \"rgb\", \"audio\", \"labels\"]]\n","video_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>video_id</th>\n","      <th>rgb</th>\n","      <th>audio</th>\n","      <th>labels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-0AjrnQ0N3A</td>\n","      <td>[[-1.9921875, -0.8627756834030151, 0.721538066...</td>\n","      <td>[[0.7215380668640137, -1.5686581134796143, -0....</td>\n","      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0MpgkddrY4</td>\n","      <td>[[0.0313420295715332, -0.28238344192504883, 1....</td>\n","      <td>[[1.3019304275512695, 0.925459623336792, 1.458...</td>\n","      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-02RMEBtLDo</td>\n","      <td>[[-0.7686580419540405, -0.47061872482299805, 2...</td>\n","      <td>[[1.082322597503662, 1.7568323612213135, 1.929...</td>\n","      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-00LLAtj0JE</td>\n","      <td>[[-1.9921875, 0.6587929725646973, -0.752971768...</td>\n","      <td>[[0.7215380668640137, -1.5686581134796143, -0....</td>\n","      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-07wapPiIAg</td>\n","      <td>[[-1.1921874284744263, 1.1450676918029785, 0.8...</td>\n","      <td>[[0.7215380668640137, -1.2235599756240845, -0....</td>\n","      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      video_id                                                rgb  \\\n","0  -0AjrnQ0N3A  [[-1.9921875, -0.8627756834030151, 0.721538066...   \n","1  -0MpgkddrY4  [[0.0313420295715332, -0.28238344192504883, 1....   \n","2  -02RMEBtLDo  [[-0.7686580419540405, -0.47061872482299805, 2...   \n","3  -00LLAtj0JE  [[-1.9921875, 0.6587929725646973, -0.752971768...   \n","4  -07wapPiIAg  [[-1.1921874284744263, 1.1450676918029785, 0.8...   \n","\n","                                               audio  \\\n","0  [[0.7215380668640137, -1.5686581134796143, -0....   \n","1  [[1.3019304275512695, 0.925459623336792, 1.458...   \n","2  [[1.082322597503662, 1.7568323612213135, 1.929...   \n","3  [[0.7215380668640137, -1.5686581134796143, -0....   \n","4  [[0.7215380668640137, -1.2235599756240845, -0....   \n","\n","                                              labels  \n","0  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, ...  \n","1  [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...  \n","2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n","3  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n","4  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  "]},"metadata":{"tags":[]},"execution_count":33}]},{"metadata":{"deletable":true,"editable":true,"id":"LaRgJZ2ZnHGQ","colab_type":"code","colab":{},"outputId":"9e14c79f-1311-4eb7-e864-f58c2d3bcfee"},"cell_type":"code","source":["# Export dataframe to BigQuery\n","import datalab.bigquery as bq\n","bigquery_dataset_name = 'ryan_youtube'\n","bigquery_table_name = 'tbl_frame_level'\n","\n","# Define BigQuery dataset and table\\n\",\n","dataset = bq.Dataset(bigquery_dataset_name)\n","table = bq.Table(bigquery_dataset_name + '.' + bigquery_table_name)\n","\n","# Create BigQuery dataset\n","if not dataset.exists():\n","    dataset.create()\n","\n","# Create or overwrite the existing table if it exists\\n\",\n","table_schema = bq.Schema.from_dataframe(video_df)\n","table.create(schema = table_schema, overwrite = True)\n","\n","video_df.to_gbq(destination_table = bigquery_dataset_name + '.' + bigquery_table_name, project_id = \"qwiklabs-gcp-8d3d0cd07cef9252\", if_exists = \"replace\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\n","\n","\n","Load is 100% Complete\n","\n","\n"],"name":"stdout"}]},{"metadata":{"deletable":true,"editable":true,"id":"X6kLyoM7nHGT","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create SQL query\n","query=\"\"\"\n","SELECT\n","  video_id,\n","  rgb,\n","  audio\n","FROM\n","  `qwiklabs-gcp-8d3d0cd07cef9252.ryan_youtube.tbl_frame_level`\n","LIMIT\n","  3\n","\"\"\""],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"eNjsGvf1nHGX","colab_type":"code","colab":{},"outputId":"c0745fd9-a2b9-449f-87e6-7c1248a51581"},"cell_type":"code","source":["# Export BigQuery results to dataframe\n","import google.datalab.bigquery as bq2\n","df_predict = bq2.Query(query).execute().result().to_dataframe()\n","df_predict.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>video_id</th>\n","      <th>rgb</th>\n","      <th>audio</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-0FDy3F9Gqo</td>\n","      <td>[[-0.21963834762573242, 1.1293814182281494, 0....</td>\n","      <td>[[-0.7372854948043823, 0.42349886894226074, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0CK7kXtP-U</td>\n","      <td>[[-1.9921875, -0.8627756834030151, 0.721538066...</td>\n","      <td>[[-0.8314031362533569, -0.21963834762573242, -...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-0QTVHnvT90</td>\n","      <td>[[-1.9921875, -0.5804227590560913, 0.439185142...</td>\n","      <td>[[0.5803616046905518, -0.7215992212295532, 0.5...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      video_id                                                rgb  \\\n","0  -0FDy3F9Gqo  [[-0.21963834762573242, 1.1293814182281494, 0....   \n","1  -0CK7kXtP-U  [[-1.9921875, -0.8627756834030151, 0.721538066...   \n","2  -0QTVHnvT90  [[-1.9921875, -0.5804227590560913, 0.439185142...   \n","\n","                                               audio  \n","0  [[-0.7372854948043823, 0.42349886894226074, -0...  \n","1  [[-0.8314031362533569, -0.21963834762573242, -...  \n","2  [[0.5803616046905518, -0.7215992212295532, 0.5...  "]},"metadata":{"tags":[]},"execution_count":64}]},{"metadata":{"deletable":true,"editable":true,"id":"-1FXyMOAnHGa","colab_type":"text"},"cell_type":"markdown","source":["### Local prediction from local model"]},{"metadata":{"deletable":true,"editable":true,"id":"qXD08uAenHGa","colab_type":"code","colab":{}},"cell_type":"code","source":["# Format dataframe to new line delimited json strings and write out to json file\n","with open('frame_level.json', 'w') as outfile:\n","  for idx, row in df_predict.iterrows():\n","    json_string = \"{\\\"video_id\\\": \\\"\" + row[\"video_id\"] + \"\\\", \\\"rgb\\\": \\\"\" + row[\"rgb\"].replace(\" \",\"\").replace(\"[\",\"\").replace(\"]\",\"\") + \"\\\", \\\"audio\\\": \\\"\" + row[\"audio\"].replace(\" \",\"\").replace(\"[\",\"\").replace(\"]\",\"\") + \"\\\"}\"\n","    outfile.write(\"%s\\n\" % json_string)"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"tTL-Yt_AnHGc","colab_type":"code","colab":{},"outputId":"87d0bf0c-e78d-4519-e42c-05053e4dcc3f"},"cell_type":"code","source":["%bash\n","model_dir=$(ls $PWD/trained_model/export/exporter | tail -1)\n","gcloud ml-engine local predict \\\n","    --model-dir=$PWD/trained_model/export/exporter/$model_dir \\\n","    --json-instances=./frame_level.json"],"execution_count":0,"outputs":[{"output_type":"stream","text":["CLASSES             LOGITS                                                                                             PREDICTIONS                PROBABILITIES\n","[0, 9, 2, 43, 15]   [6.291934967041016, 6.258187294006348, 6.062037944793701, 5.9421892166137695, 5.871412754058838]   [1.0, 1.0, 1.0, 1.0, 1.0]  [0.9981522560119629, 0.9980889558792114, 0.9976757764816284, 0.9973806142807007, 0.9971890449523926]\n","[9, 43, 0, 15, 19]  [8.108847618103027, 7.53411865234375, 7.450527191162109, 7.245649814605713, 7.1199798583984375]    [1.0, 1.0, 1.0, 1.0, 1.0]  [0.9996992349624634, 0.9994657635688782, 0.9994192123413086, 0.9992872476577759, 0.9991918802261353]\n","[9, 0, 2, 43, 15]   [10.908120155334473, 10.618941307067871, 10.082742691040039, 9.946921348571777, 9.76628589630127]  [1.0, 1.0, 1.0, 1.0, 1.0]  [0.999981701374054, 0.9999755620956421, 0.9999582171440125, 0.9999521374702454, 0.9999426603317261]\n"],"name":"stdout"},{"output_type":"stream","text":["WARNING: /usr/local/envs/py2env/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n","  from ._conv import register_converters as _register_converters\n","2018-05-25 05:30:01.507851: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n","\n"],"name":"stderr"}]},{"metadata":{"deletable":true,"editable":true,"id":"2t9DCxglnHGf","colab_type":"text"},"cell_type":"markdown","source":["### GCloud ML-Engine prediction from deployed model"]},{"metadata":{"deletable":true,"editable":true,"id":"wqlI_IfgnHGf","colab_type":"code","colab":{}},"cell_type":"code","source":["# Format dataframe to instances list to get sent to ML-Engine\n","instances = [{\"video_id\": row[\"video_id\"], \"rgb\": row[\"rgb\"].replace(\" \",\"\").replace(\"[\",\"\").replace(\"]\",\"\"), \"audio\": row[\"audio\"].replace(\" \",\"\").replace(\"[\",\"\").replace(\"]\",\"\")} for idx, row in df_predict.iterrows()]"],"execution_count":0,"outputs":[]},{"metadata":{"deletable":true,"editable":true,"id":"50NWw2ePnHGh","colab_type":"code","colab":{},"outputId":"8d279b56-0f03-4c4f-c333-52d3137b9b79"},"cell_type":"code","source":["# Send instance dictionary to receive response from ML-Engine for online prediction\n","from googleapiclient import discovery\n","from oauth2client.client import GoogleCredentials\n","import json\n","\n","credentials = GoogleCredentials.get_application_default()\n","api = discovery.build('ml', 'v1', credentials=credentials)\n","\n","request_data = {\"instances\": instances}\n","\n","parent = 'projects/%s/models/%s/versions/%s' % (PROJECT, 'youtube_8m_frame_level_datasets', 'v1')\n","response = api.projects().predict(body = request_data, name = parent).execute()\n","print(\"response = {}\".format(response))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["response = {u'predictions': [{u'probabilities': [1.0, 1.0, 1.0, 1.0, 1.0], u'logits': [inf, inf, inf, inf, inf], u'classes': [0, 1, 2, 3, 4], u'predictions': [1.0, 1.0, 1.0, 1.0, 1.0]}, {u'probabilities': [1.0, 1.0, 1.0, 1.0, 1.0], u'logits': [inf, inf, inf, inf, inf], u'classes': [0, 1, 2, 3, 4], u'predictions': [1.0, 1.0, 1.0, 1.0, 1.0]}, {u'probabilities': [1.0, 1.0, 1.0, 1.0, 1.0], u'logits': [inf, inf, inf, inf, inf], u'classes': [0, 1, 2, 3, 4], u'predictions': [1.0, 1.0, 1.0, 1.0, 1.0]}]}\n"],"name":"stdout"}]},{"metadata":{"id":"dxSfk1BZnHGj","colab_type":"code","colab":{}},"cell_type":"code","source":[""],"execution_count":0,"outputs":[]}]}